As the number of users connected to mobile networks increases, so too does the need to ensure reliable and efficient operation of mobile network equipment. Therefore, mobile network operators typically test mobile network equipment using dedicated test equipment that simulates various network conditions before the equipment is deployed in a live network. Simulated network conditions may include, for example, simulating a maximum number of user equipment devices or UEs capable of simultaneously registering/de-registering with the network, simulating inter-/intra-mobile network equipment handover, transmitting bearer traffic, and/or combinations thereof. Generally, mobile network equipment testing may be divided into three categories: functional correctness testing (e.g., protocol validation and compliance), inter-system compatibility and integration testing, and stress testing.
For many mobile communications networks, such as long term evolution (LTE) networks and other emerging mobile network technologies, mobile network equipment testing has primarily focused on testing for functional correctness and inter-system compatibility of mobile network equipment. For example, the evolved Node B or eNodeB is a mobile network entity in LTE networks that has functionality similar to that of a base station in 2G networks or a Node B in 3G mobile networks. The eNB communicates directly with LTE UEs and is responsible for header compression, ciphering, reliable delivery of packets, admission control, and radio resource management. According to common public radio interface (CPRI) specifications, mobile network equipment such as BSs, Node Bs, and eNBs may be logically decomposed into a radio equipment controller (REC) and one or more radio equipment (RE) components being connected together via a CPRI link. Because testing for functional correctness and inter-system compatibility of mobile network equipment does not typically require simulating more than one UE at a time, conventional mobile network equipment testing has focused on simulating a single UE and its air interface in great detail. As a result, methods for simulating large numbers of UEs needed for stress testing a mobile network device (e.g., hundreds or thousands of UEs) have been limited. In order to address this shortcoming, conventional methods for stress testing mobile network equipment have attempted to leverage existing models of individual UEs and the associated air interface by multiplying the number of individual UE simulators or by using banks of real UE devices.
However, one problem associated with conventional models for testing mobile network equipment is that modeling individual UEs and their associated communications over a simulated air interface is not scalable to the number of UEs needed for stress testing a typical mobile network device. For example, using conventional methods, the same fixed cost is associated with simulating each individual UE. As a result, the cost of fully and accurately stress testing a single mobile network device scales linearly with the number of simulated UEs, and therefore becomes prohibitively expensive for large numbers of simulated UEs. As a result, conventional methods are typically limited to simulating only a fraction of per-sector or total mobile network equipment capacity before cost or technical limitations prevent further scaling and thus are not effective for stress testing.
Accordingly, in light of these difficulties, a long felt need exists for improved methods, systems, and computer readable media for performing accurate and highly scalable stress testing of mobile network equipment nodes, such as eNBs.